Electrical signals often include an undistorted signal component and a noise component, where the noise arises from several sources, including white or Gaussian noise, temperature-induced Johnson noise, impulse noise, cross-talk noise, echo noise, intermodulation noise, amplitude noise, jitter noise, uncontrolled voltage variation, and several varieties of signal distortion. Gaussian noise has been studied widely, and many techniques exist to control or reduce the effects of Gaussian noise. Impulse noise has received less attention.
Klund, in U.S. Pat. No. 3,541,458, discloses use of an adaptive array of sensors to detect a weak (desired) signal in the presence of noise. The amplitude weights and phases assigned to the different sensors are varied to produce a signal that, ideally, is free of the spatially stationary component of the noise field. The invention uses a Fourier series representation for the waveform produced by each sensor and uses time averaging and other manipulations to identify the optimal amplitudes and phases assigned to each sensor.
An adaptive broadband noise cancellation signal receiver is disclosed in U.S. Pat. No. 4,177,430, issued to Paul. A desired radiofrequency passband is selected, and an input signal in this band is converted to an intermediate frequency broadband signal. The converted input signal is passed through two parallel signal processing channels having differing frequency characteristics. The first channel output represents desired signal plus noise, and the second channel output represents primarily noise, after filtering by an adaptive transversal filter. The two channel outputs are then subtracted from each other to produce a waveform that is primarily the desired signal. The adaptive filter shuts off if the first and second channel outputs have no audio component in common and emphasizes any broadband noise that is present.
Eaton et al disclose a circuit for suppression of narrowband interference noise (as opposed to white noise), in U.S. Pat. No. 4,287,475. The received signal, containing the desired signal plus narrowband noise plus wideband noise, is discrete Fourier transformed using a chirp algorithm illustrated in Fourier space in FIG. 3. Sidelobes are suppressed by about 40 dB relative to a center peak by additional filtering. The resulting signal is smoothed to produce a power spectral density function, apparently representing the narrowband noise, which is used to notch out this noise in the time domain.
U.S. Pat. No. 4,537,200, issued to Widrow, discloses a system for electrocardiogram signal enhancement by adaptive cancellation of noise created by operation of an electro-surgical instrument, such as a "Bovie", used for cauterizing tissue cut by an adjacent surgical knife. Gross interference noise is filtered out by use of passive lowpass and active lowpass filters. A first waveform, consisting of the desired signal plus a first noise signal, and a second waveform, consisting of a second noise signal only, are formed, with the two noise waveforms being correlated with each other but not with the desired signal. The second noise waveform only is passed through an adaptive filter, which consists of a tapped delay line, with the tapped off signals being weighted and summed to form a Least Mean Squares (LMS) estimate of the first noise waveform. The first and second waveforms are then subtracted from each other. The resulting difference signal, representing the desired signal, is fed back to the adaptive filter to determine, in a time varying manner, an LMS estimate of the first noise waveform.
Zinser et al, in U.S. Pat. No. 4,649,505, disclose a two-input adaptive noise canceller that also begins with a first signal (desired signal plus first noise signal) and a second (noise only) signal, where the two noise signals are highly correlated. As best illustrated in FIG. 2, the second signal is passed through a first adaptive filter to produce a first "noise only" signal that is subtracted from the first signal (desired signal plus first noise signal). This first difference signal is then passed through a second adaptive filter, the output thereof is subtracted from the second (noise only) signal, and this second difference signal is used to adjust the second adaptive filter to improve the apparatus output signal, which is the first difference signal. The inventors suggest that this approach is needed to remove any vestiges of the desired signal that may be present in the second (noise only) signal.
An adaptive radar signal processor that cancels a clutter or noise signal that is present is disclosed in U.S. Pat. No. 4,719,466, issued to Farina et al. The apparatus is applied to distinguish a desired echo radar signal from a clutter signal and from thermal noise (both Gaussian) that are part of the return signal. A matrix D representing a steady state estimate is constructed by statistical manipulations, and an invertible covariance matrix M is constructed by the relation M.sup.-1 =D.sup.T D*, where D.sup.T and D* are the transpose and complex conjugate of the matrix D. The initial signal uses time delay, multiplication and integration of various signal to determine a covariance matrix.
Yoshida discloses an adaptive jitter noise canceller in U.S. Pat. No. 4,792,964. An initial signal, containing the desired signal and superimposed jitter noise, is passed through a low pass filter, then sampled at a uniformly spaced sequence of times {t.sub.0 +n.DELTA.t.sub.0 } (n=0, 1, 2, . . . ; N a positive integer), then passed through a sinusoidal accentuator. The output signal is passed through an interpolator that produces quasi-samples at a sequence of times {t.sub.1 +n.DELTA.t.sub.1 }, then passed through a second lowpass filter, then passed through a prediction filter to produce a filter output signal. This filter output signal is then subtracted from the initial signal to cancel the jitter noise.
U.S. Pat. No. 4,914,398, issued to Jove et al, disclose a system that suppresses additive signal disturbances in data channels that contain magneto-resistive transducers. The system passes an initial signal through a time delay and also passes the initial signal through positive and negative signal envelope detectors that are subtracted from each other to produce a second signal. This second signal is passed through a nonlinear adaptive filter to produce an estimate of the disturbance present. The adaptive filter output signal is subtracted from the time delayed initial signal to produce a difference signal that suppresses the additive disturbance.
An adaptive pre-transmission filter for a modem, computed from the observed noise spectrum of the transmission channel, is disclosed by Betts et al in U.S. Pat. No. 5,008,903. A noise spectrum analyzer positioned at the receiver end calculates the difference between the transmitted signal and the received signal, and this difference is discretely Fourier transformed to produce an estimated noise spectrum. This noise spectrum is then transmitted back to the transmitter on a secondary channel, and pre-transmit filter coefficients are determined and used to suppress the observed transmission channel noise.
In U.S. Pat. No. 5,018,088, Higbie discloses an adaptive, locally-optimum signal detector and processor for spread spectrum communications. Here, the noise strength is much greater than the initial strength of the desired signal. The amplitude probability distribution function (APDF) of the noise is determined approximately, using the APDF of the desired signal plus noise as an estimate. This APDF estimate is then manipulated mathematically to produce one or more spectra that represent the noise alone, and a signal with enhanced desired signal component is produced. This approach changes adaptively with change in the present noise statistics.
Widrow et al, in "Adaptive Noise Cancelling: Principles and Applications", I.E.E.E. Proc., vol. 63 (1975) pp. 1692-1716, disclose use of a first or primary signal containing desired signal plus noise s(t)+n.sub.1 (t) and a second, noise-only signal n.sub.2 (t), where the two noise signals have non-zero correlation with each other but have zero correlation with the desired signal. The second signal is passed through an adaptive filter whose output signal is subtracted from the first signal, and the output difference signal is used to adjust the adaptive filter to produce an output difference signal in which the first noise component n.sub.1 (t) is cancelled as completely as possible, using a Least Mean Squares algorithm to maximize noise cancellation. The basic problem can be generalized somewhat by adding third and fourth noise components n.sub.3 (t) and n.sub.4 (t) to the first and second signals, where these third and fourth noise components are uncorrelated with all other signals. This basic approach, where n.sub.3 (t)=n.sub.4 (t)=0, is used in many of the patents discussed above and in the invention. This article also discusses possible approaches when the second signal has non-zero correlation with the desired signal, a problem also discussed in the Zinsser et al patent (4,649,505) above.
In "Adaptive Noise Canceling Applied to Sinusoidal Interferences", I.E.E.E. Trans. Acoustics, Speech and Signal Processing, vol. 25 (1977) pp. 484-491, Glover discloses a method for eliminating sinusoidal or other periodic interferences that distort a desired signal. The basic approach discussed by Widrow et al in the 1975 article, and the adaptive filter with feedback is approximated by a linear, time-invariant filter. The noise or interference is a sinusoidal term or sum of such terms, whose presence is suppressed by appropriate choices of complex weighting coefficients in a related filter function.
Zeidler et al disclose a method for "Adaptive Enhancement of Multiple Sinusoids in Uncorrelated Noise", I.E.E.E. Trans. Acoustics, Speech and Signal Processing, vol;. 26 (1978) pp. 240-254. Here, the noise is white noise and the desired signal is a finite sum of sinusoids with arbitrary frequencies. The method involves approximate inversion of certain scalar or matrix equations to determine certain weighting coefficients that define the desired signal.
Sambur, in "Adaptive Noise Canceling for Speech Signals", I.E.E.E. Trans. Acoustics, Speech and Signal Processing, vol. 26 (1978) pp. 419-423, discloses an adaptive filtering technique that cancels out the desired signal, leaving a pure noise signal that can, presumably, be subtracted from the original combined signal to provide the desired signal. The method uses a variant on the usual Least Mean Squares (LMS) approach discussed in the Widrow et al 1975 article.
Ferrara and Widrow, in "The Time Sequenced Adaptive Filter", I.E.E.E. Trans. Acoustics, Speech and Signal Processing, vol. 29 (1981) pp. 679-683, disclose an alternative to the LMS filter, using a time-sequenced filter and associated algorithm. The algorithm provides a different weight matrix or vector for each of a recurring sequence of error surfaces defined by a least mean square error computed in the basic approach.
What is needed is an adaptive signal processing method that, ideally at least, will result in cancellation of most or all impulse noise and that does not require technically complex processing of the input signal. Preferably, the method should accept and process an input signal in real time, with little or no delay in issuance of an output signal with most or all of the noise removed.